Open Source Python Earth Sciences Software
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Python Earth Sciences Software
View 165 business solutionsBrowse free open source Python Earth Sciences Software and projects below. Use the toggles on the left to filter open source Python Earth Sciences Software by OS, license, language, programming language, and project status.
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--- IMPORTANT : This project has been moved to GitHub at https://github.com/clstoulouse/motu-client-python. Download the last version from the release page https://github.com/clstoulouse/motu-client-python/releases. --- Motu is a high efficient and robust Web Server which fills the gap between heterogeneous Data Providers to End Users. Motu handles, extracts and transforms oceanographic huge volumes of data without performance collapse. This client enables to extract and download data through a python command line Indesol project sample: http://www.indeso.web.id/indeso_wp/index.php/faq/30-6-how-to-write-and-run-the-script-to-download-indeso-met-ocean-data
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GDAL wheels for linux
GDAL wheels for python and C/C++ projects (Linux only)
To use precompiled wheels: 1) go to releases (Files) and download tarball needed; 2) install it with command: python3 -m pip install /path/to/wheel.whl Or simply use URL in pip: python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download URL may be found under "View details" button (i) To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages) To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux Usually this is done via command `make wheels` GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal Built with PROJ (proj.db is included), GEOS, EXPAT. See Dockerfile.wheels for additional components. -
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ccplot
CloudSat and CALIPSO plotting tool
ccplot is an open source command-line program for plotting profile, layer and earth view data sets from CloudSat, CALIPSO and Aqua MODIS products. -
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
DeepChem aims to provide a high-quality open-source toolchain that democratizes the use of deep learning in drug discovery, materials science, quantum chemistry, and biology. DeepChem currently supports Python 3.7 through 3.9 and requires these packages on any condition. DeepChem has a number of "soft" requirements. If you face some errors like ImportError: This class requires XXXX, you may need to install some packages. Deepchem provides support for TensorFlow, PyTorch, JAX and each requires an individual pip Installation. The DeepChem project maintains an extensive collection of tutorials. All tutorials are designed to be run on Google collab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence that will take you from beginner to proficient at molecular machine learning and computational biology more broadly. -
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weewx weather software
Python program for getting the most from your weather station
WeeWX is a free, open source, software program, written in Python, which interacts with your weather station to produce graphs, reports, and HTML pages. Downloads can be found at http://weewx.com/downloads Development happens at https://github.com/weewx/weewx -
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Madagascar is a software package for geophysical data processing and reproducible numerical experiments. The package mission is to provide a convenient environment for researchers working with digital image and data processing in geophysics and related fields.
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GPlates
Interactive visualization of plate tectonics.
GPlates is a plate-tectonics program. Manipulate reconstructions of geological and paleo-geographic features through geological time. Interactively visualize vector, raster and volume data. PyGPlates is the GPlates Python library. Get fine-grained access to GPlates functionality in your Python scripts. -
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Astropy
Repository for the Astropy core package
The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages. Astropy is a Python library for use in astronomy. Learn Astropy provides a portal to all of the Astropy educational material through a single dynamically searchable web page. It allows you to filter tutorials by keywords, search for filters, and make search queries in tutorials and documentation simultaneously. The Anaconda Python Distribution includes Astropy and is the recommended way to install both Python and the Astropy package. The astropy package contains key functionality and common tools needed for performing astronomy and astrophysics with Python. It is at the core of the Astropy Project, which aims to enable the community to develop a robust ecosystem of affiliated packages covering a broad range of needs for astronomical research, data processing, and data analysis. -
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Graphical Forecast Editor
View and edit grids of meteorological values
Allows viewing and editing of weather data from weather models as two dimensional grids. Python scripting support hooks in the ingest, editing and product creation subsystems allow for customization. All network communication made through HTTP services. Enhanced version of the system used by national weather services. -
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ALAMODE
Ab initio simulator for thermal transport and lattice anharmonicity
ALAMODE is designed for analyzing lattice anharmonicity and lattice thermal conductivity of solids. By using an external DFT package such as VASP and Quantum ESPRESSO, you can extract harmonic and anharmonic force constants straightforwardly with ALAMODE. Using the anharmonic force constants, you can also calculate lattice thermal conductivity from first principles. For more information about ALAMODE, please visit the following webpages: Documentation : http://alamode.readthedocs.io/en/latest/ git repository : https://github.com/ttadano/alamode -
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Spectral Python
A python module for hyperspectral image processing
Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. -
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GEOMS2
Geostatistics and geosciences modeling software
GEOMS2 is a geostatistics and geosciences modeling software. Provides interface for grid (mesh), point, surface and data (non-spatial) objects. It has a 3D viewer and 2D plots using the well known Python engines Mayavi and Matplotlib. It has several functions to manipulate your data as well as provide univariate and multivariate analysis. NOTE: The software is still an early beta. Please tell us if you found a bug. Download datasets for students of Geostatistics 2017 (Petroleum Engineering): https://sourceforge.net/projects/geoms2/files/Geostatistics_Petroleum.zip/download Download datasets for students of Geostatistics 2017 (Geology and Mining Engineering): https://sourceforge.net/projects/geoms2/files/Geostatistics_Mining.zip/download Old: http://sourceforge.net/projects/geoms2/files/SETS_geoestatistica2_2015.7z/download https://sites.google.com/site/cmrpsoftware/downloads/Quarry_sets.7z?attredirects=0&d=1 http://sourceforge.net/projects/geoms2/files/Mining.7z/download -
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CAIRO for AERMOD
AERMOD, visualisation, input, modelling and compiling tool
CAIRO for AERMOD v1.1 by MSc Dominik Subotić Simplified training software Avaliable: www.sourceforge.net/projects/cairo-for-aermod/ QGIS plugin: CAIROforAERMOD (Coming 2025.) Tutorial: https://www.youtube.com/watch?v=DZnsJuu1zLc AERMAP, AERMOD and AERPLOT analysis tool and input file compiler. Features: Automatic input by copying coordinates (Google Maps or text) and automatic conversion to UTM. Sources are automatically visualised in Google Earth. Input is done through user interface and automatically compiled into correct syntax and project/file structure. Windows application, supports up to 3 averaging periods, maxtable, maxifile, rankfile and plotfile. Elevation and meteorological data is third party. CAIRO for AERMOD (Compile AERMAP, AERMOD and AERPLOT Input and Run Output) aimed at streamlining the process of making complex input file, with unique syntax and running them. While obviating the need to manually write inputs and running the program through Shell -
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jif3d
3D Joint Inversion Framework
This repository is now outdated. The current version of jif3D is available from https://git.tu-berlin.de/applied-geophysics/jif3d -
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MOSSCO
Modular System for Shelves and Coasts
The Modular System for Shelves and Coasts (MOSSCO) is a coupling framework for Earth System Models. It helps users to integrate their own numerical models with other developments. Quick Start: git clone git://git.code.sf.net/p/mossco/setups mossco-setups git clone git://git.code.sf.net/p/mossco/code mossco-code cd mossco-code cat QuickStart.md or read online https://sf.net/p/mossco/code/ci/master/tree/QuickStart.md Beware: this is alpha software, expect trouble! ------------------------- Better, read the full documentation online http://www.mossco.de/doc or make it yourself make doc acroread doc/mossco_documentation.pdf ---------------------- Why MOSSCO? MOSSCO, the "Modular System for Shelves and Coasts" is a framework for coupling processes or domains that are originally developed in standalone numerical models. The software MOSSCO implements this infrastructure in the form of a library of components and couplers, and of example coupled application. -
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PEng is a tool for performing and documenting engineering calculations. It can handle units and generates output via Latex.
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openGPR is a framework for the management, processing and 2D/3D-visualization of ground penetrating radar (GPR, georadar) data. It interoperates with existing tools like Seismic Unix, with a special focus on georadar applications.
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PySptools
Hyperspectral algorithms for Python
A lightweight hyperspectral imaging library that provides developers with spectral algorithms for the Python programming language. New for v0.14.x: a scikit-learn bridge (alpha and partial). The functions and classes are organized by topics: * abundance maps: FCLS, NNLS, UCLS * classification: AbundanceClassification, NormXCorr, KMeans SAM, SID, SVC * detection: ACE, CEM, GLRT, MatchedFilter, OSP * distance: chebychev, NormXCorr, SAM, SID * endmembers extraction: ATGP, FIPPI, NFINDR, PPI * material count: HfcVd, HySime * noise: Savitzky Golay, MNF, whiten * sigproc: bilateral * sklearn: HyperEstimatorCrossVal, HyperSVC and others * spectro: convex hull quotient, features extraction (tetracorder style), USGS06 lib interface * util: load_ENVI_file, load_ENVI_spec_lib, corr, cov and others The library do an extensive use of the numpy numeric library and can achieve good speed. The library is mature enough and is very usable even if the development is at a beta step. -
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RFlex
Spectroradiometer control for shipborne reflectance measurements
Spectroradiometer control for autonomous reflectance measurements from moving vessels. The software incorporates GPS signal parsing, control of the sensor platform viewing angle (stepper motor control), sensor triggering and data recording. Continue to the download section for the latest release information Rrs data processing code (the 'fingerprint technique') is also available - but moved to my github page: https://github.com/StefanSimis -------- Oct 2014: a beta version is available (20141006) Sep 2014 news: The latest version is 20140723, addressing some bugs. See the change log. Enjoy! Two Rflex units collected over 30,000 validated Rrs spectra on the Baltic Sea this year! See http://www.ferryscope.org for dissemination of these results in the coming years. ------ Licence: http://creativecommons.org/licenses/by-nc-sa/3.0/ -
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UV-CDAT
Ultrascale Visualization Climate Data Analysis Tools
Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), is a powerful and complete front-end to a rich set of visual-data exploration and analysis capabilities well suited for climate-data analysis problems. -
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GrainSizeTools script
A Python script for estimating the grain size from thin sections
Homepage & docs: http://marcoalopez.github.io/GrainSizeTools/ GrainSizeTools is a free, open-source, cross-platform script written in Python that provides several tools for (1) estimating average grain size in polycrystalline materials, (2) characterizing the nature of the distribution of grain sizes (either from apparent distributions or approximating 3D grain size distributions via stereology), and estimating differential stress via paleopizometers. The script requires as the input the areas of the grain profiles measured grain-by-grain on planar sections and does not require previous experience with Python programming language (see documentation below and FAQ). For users with coding skills, the script is organized in a modular way facilitating the reuse and code extension. Lopez-Sanchez, MA (2018). GrainSizeTools: a Python script for grain size analysis and paleopiezometry based on grain size. Journal of Open Source Software, 3(30), 863, https://doi.org/10.21105/joss.00863 -
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WARNING! HPGL source code (NOT the releases, they are still here) now moved to the GitHub at https://github.com/hpgl/hpgl. Please, update your bookmarks!
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PyRadmon Reborn
PyRadmon, a Python script to use on both Linux and Windows.
PyRadmon Reborn. This project started after the original programmers quit supporting it. The project now contains support for audio*, has a version that supports two Geiger counters** and a cleaner, more stable code. * = PyAudio is used as library for the audio support. ** = The two counters both run in their own thread. UPDATE-2015-2: * Added timeouts to sockets to prevent crashing of the script. * Updated some comments. UPDATE-2015-1: Since there have been some passed time I decided to pickup the keyboard to write for PyRadmon again, hence the new updates. Most significant changes: * Added a logger for catching and storing messages to a log file. * Complete cosmetic overhaul, fixing ugliness throughout the code, in the hope others might find it easier to read/more standardized.
